Unveiling Phenotype–Genotype Interplay with Deep Learning Foundation Models for scRNA-seq: A Quantitative Perspective

Foundation models have emerged as powerful tools for analyzing single-cell RNA sequencing (scRNA-seq) data, leveraging large-scale pretraining to capture complex gene expression patterns. However, a comprehensive quantitative framework for understanding the interplay between phenotypes and genotypes remains underdeveloped. Such a framework is critical not only for validating model performance but also for uncovering previously unrecognized biological relationships. In this work, we present both traditional and deep learning-based quantitative analysis pipelines for PolyGene [1], a transformer-based scRNA-seq foundation model, aimed at disentangling the complex phenotype–genotype relationship. First, we implement a top-k classification and entropy evaluation pipeline to serve as a primary validation framework. Our results demonstrate that the pretrained PolyGene [1] is robust in top-k classification metrics and provides meaningful insights into the entropy landscape of human cells across different life stages. Second, we propose a novel deep learning gradientbased gene selection method designed to address limitations in traditional feature selection approaches, such as poor scalability and sensitivity to heterogeneity in high-dimensional data. Through empirical evaluations on benchmark scRNA-seq datasets, we show that our method enhances model interpretability and improves downstream performance, offering a more scalable and biologically relevant alternative to existing techniques. Overall, this work introduces a set of quantitative analysis tools that fill a critical gap in evaluating and interpreting scRNA-seq foundation models, contributing to a deeper understanding of the genotype–phenotype interplay through modern deep learning techniques.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Thadawasin, Pakaphol

Advisor dc:contributor.advisor

• Edelman, Elazer R.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162920

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162920